[BioC] Using DESeq or EdgeR for Exon Differential Expression Analysis

[Wei Shi]

There are more than 20% of human exons which have length less than 50 bases. These exons are very likely to have very small number of reads mapped to them. But my understanding is that low count features (such as genes, exons, ...) will be filtered out before the differential expression analysis is performed.

Wei

On Apr 1, 2011, at 9:59 AM, Naomi Altman wrote:

> There should not be a problem as long as you have read counts for each exon.  The main issue is that you have little power if the number of reads for a feature is small.  So you will need high coverage.  You might want to use a normalization method such as the quantile method in edgeR, as I am not sure the others have been tested for this type of data.  (
> 
> --Naomi
> 
> At 02:18 PM 3/31/2011, adeonari at mrc-lmb.cam.ac.uk wrote:
>> Hello Bioconductor community,
>> 
>> We were wondering if it would be possible to perform differential
>> expression analysis of exon expression using DESeq or EdgeR. Would the
>> statistical assumptions be the same, and has anyone attempted this type of
>> analysis? Any feedback or insights would be really appreciated!
>> 
>> Cheers,
>> 
>> Andrew
>> 
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